12 citations · 18 across the 8 of their papers we have counts for
8 papers · 1 filter
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification
Xin Jin, Tianyu He, Xu Shen +5
Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering…
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Xin Jin, Tianyu He, Kecheng Zheng +7
Cloth-Changing person re-identification (CC-ReID) aims at matching the same person across different locations over a long-duration, e.g., over days, and therefore inevitably meets…
Dense Interaction Learning for Video-based Person Re-identification
Tianyu He, Xin Jin, Xu Shen +3
Video-based person re-identification (re-ID) aims at matching the same person across video clips. Efficiently exploiting multi-scale fine-grained features while building the struct…
Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition
Zhen Huang, Xu Shen, Xinmei Tian +3
Skeleton-based human action recognition has attracted much attention with the prevalence of accessible depth sensors. Recently, graph convolutional networks (GCNs) have been widely…
Transform-Invariant Convolutional Neural Networks for Image Classification and Search
Xu Shen, Xinmei Tian, Anfeng He +2
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invaria…
Patch Reordering: a Novel Way to Achieve Rotation and Translation Invariance in Convolutional Neural Networks
Xu Shen, Xinmei Tian, Shaoyan Sun +1
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks. However, the combination of convolution and pooling operations…
Continuous Dropout
Xu Shen, Xinmei Tian, Tongliang Liu +2
Dropout has been proven to be an effective algorithm for training robust deep networks because of its ability to prevent overfitting by avoiding the co-adaptation of feature detect…
Quantization Networks
Jiwei Yang, Xu Shen, Jun Xing +5
Although deep neural networks are highly effective, their high computational and memory costs severely challenge their applications on portable devices. As a consequence, low-bit q…